Consistent Document-level Relation Extraction via Counterfactuals

Many datasets have been developed to train and evaluate document-level relation extraction (RE) models. Most of these are constructed using real-world data. It has been shown that RE models trained on real-world data suffer from factual biases. To evaluate and address this issue, we present COVERED, a counterfactual data generation approach for documentlevel relation extraction datasets using entity replacement. We first demonstrate that models trained on factual data exhibit inconsistent behavior: while they accurately extract triples from factual data, they fail to extract the same triples after counterfactual modification. This inconsistency suggests that models trained on factual data rely on spurious signals such as specific entities and external knowledge -rather than on the input context -to extract triples. We show that by generating document-level counterfactual data with COVERED and training models on them, consistency is maintained with minimal impact on RE performance. We release our COVERED pipeline 1 as well as RE-DOCRED-CF, a dataset of counterfactual RE documents, to assist in evaluating and addressing inconsistency in document-level RE.